TokSon Choe

Obstacle Avoidance for Wheeled Robots in Unknown Environments Using Model Predictive Control

Abstract This paper presents a model predictive approach for obstacle avoidance of car-like unmanned ground vehicles (UGVs). An optimal tracking problem while avoiding obstacles in unknown environments is formulated in terms of cost minimization under constraints. Information on obstacles can be incorporated online in the nonlinear model predictive framework and kinematic constraints are treated by Karush-Kuhn-Tucker (KKT) condition. The overall problem is solved real-time with nonlinear programming. This approach is applied to car-like robots including tire models while explicitly considering the dimension of the UGV rather than treating it as a dimensionless cart model. Two kinds of potential-like terms are employed in the cost function for obstacles avoidance. The first term is to consider the distance between the UGV and the obstacle, and the second one is to consider the parallax information of the UGV about the obstacles. Simulation results show that both two approaches can make safe steering in a simple environment, but in a complex environment such as an urban area, the approach based on the modified parallax (MP) was more successful in the view of the computation time and safe steering.

Communication in distributed model predictive collision avoidance

This paper presents a model predictive approach for collision avoidance of car-like robots. An optimal problem is formulated in terms of cost minimization under constraints. Information on each robot can be incorporated online in the nonlinear model predictive framework and kinematic constraints are treated by Karush-Kuhn-Tucker(KKT) condition. For distributed collision avoidance of multiple robots with two levels of a communication network, performances are compared. In comparison with different types of communication, how much information the robots share can cause difference in the performance. More successful collision avoidance was possible when the robots share enough amount of information.

A motor selection technique for designing a manipulator

This paper presents a servo motor selection technique for a manipulator design. Straete et al. s normalization method is adopted and is improved. The improvements are as follows: First, an equivalent output power lines are plotted at the same load curve, which gives another selection criterion. Second, a temperature based criterion is proposed for the continuous limit, which estimates the steady state winding temperature of a motor, and can be used to determine a suitable motor and gear ratio. This formulation gives not only the selection of a motor and gear ratio, but also gives the estimated operating temperature.

Real-time collision avoidance method for unmanned ground vehicle

In this paper, a real-time collision avoidance method is proposed for unmanned ground vehicles (UGV). To ensure real-time implementation, virtual force fields are calculated in on dimensional space. The steering force field is generated by the steering command either transmitted in the remote control station or calculated in the autonomous navigation system (ANS) of the UGV. The obstacle force field is generated by range data obtained from a laser range finder (LRF) mounted on the UGV. Using the integrated force field overlapped these steering and obstacle force fields modified steering, velocity and emergency stop commands are created to avoid obstacles and follow a planned path. The suggested method is inserted as one component in the experimental autonomous vehicle (XAV) system. Through various real experiments and tests using the XAV, the usefulness and practicality of the proposed method are verified.

Application of time-frequency domain reflectometry for measuring load impedance

In this paper, we propose a new load impedance measurement system based on time-frequency domain reflectometry. The proposed system was able to measure the load impedances that were realized with using various terminal resistors and connected at the end of a cable. The experimental results show that the proposed system allows us to know where the connected terminal resistor is on the cable, and how much the impedance of the terminal resistor is. Through real experiments, it is possible for the proposed system to acquire more accurate results in wide range of impedance than conventional commercial time domain reflectometry instrument.

Development of Target Signal Simulator for Multi-Beam Type FMCW Radar

To detect targets for autonomous navigation of unmanned ground vehicle, mounted sensors are required to work all-weather condition. In this point of view, the FMCW radar is quietly appropriate. In this paper, we present development results of target signal simulator for multi-beam type FMCW radar. A target signal simulator make pseudo target signals which simulates multiple moving targets. And we describe how to make hit information for each target in multi-beam type radar. The developed methods are utilized for target tracking device. Moreover it can be applied to similar target signal simulator.

Safe steering of UGVs in polygonal environments

This paper presents an application of a model predictive control for trajectory generation of an unmanned ground vehicle. An optimal tracking problem while avoiding collision with obstacles is formulated in terms of cost minimization under constraints. The cost function includes terms corresponding to the deviation from the desired trajectory, magnitude of the control input, proximity to the obstacles and the final destination point, respectively. Information on obstacles can be incorporated online in the nonlinear model predictive framework and the resulting constrained optimization problem can be solved using nonlinear programming techniques such as augmented Lagrangian. Then kinematic constraints are treated by the Karush-Kuhn-Tucker (KKT) condition. This approach has been applied for generating safe trajectories for the nonlinear dynamics of a vehicle with a nonlinear tire model in a 2D polygonal environment. Simulation results show that the satisfactory performance was achieved in terms of short and safe trajectory satisfying input constraints.

A Method of Fast Track Merging for Multi-Target Tracking under Heavy Clutter Environment

In this paper, we proposed a method of fast track merging which is the foundation of track to track association technique. The existing method of track merging is performed throughout comparison between tracks to tracks. Therefore, it has heavy calculation time. In our research, we developed a method for fast clustering by using nearest neighbor measurement identification. The simulation results show that the proposed method is more faster than previous method about 3.3%. We expect that this method could be effectively used in multi-target tracking particularly in heavy clutter environment.Keywords : Track Merging, Track-to-Track Association, Multi-Target Tracking, Clutter Environment

A novel approach of global path planning for UGV

Global path planning (GPP) is the generation of an optimal trajectory to efficiently move from one position to specified target position with known environment. Most of GPP methodologies offer an optimal 2D-shortest path without considering vehicle parameters on the plain environments. However, it is motivated to consider 3D terrain and vehicle parameters to enhance traversability on the rough terrain. In this paper, we propose a novel approach of GPP method for unmanned ground vehicles (UGVs) by applying distance transform (3D to 2D) based on the slope of terrain. In addition, the generated path is modified by smoothing process based on the local path planning method which considers vehicle stability on the specified candidate curve and speed. The proposed methodology is tested by simulations and shows enhanced performance.